Skip to main content
Book cover

InECCE2019 pp 379–387Cite as

Early Rubeosis Iridis Detection Using Feature Extraction Process

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 632)


Iris image analysis studies the relationship between human health and changes in the anatomy of the iris. One of the changes related to the anatomy of the iris is diabetes. This illness can be determined from the iris of human eyes because it affects the eyes. Latest advanced technologies are introduced in the image processing that helps automate the detection of diabetes based on the analysis of iris feature extractions. Various features are detected on iris such as texture, colour, histogram and shape. In this paper, the dataset of iris image from Warsaw Biobase are used to detect and recognise the rubeosis iridis by extracting their details using image processing methods. The results obtained from the experiment show that the normal and abnormal iris image can be classified using original and small size of iris image. Through this experiment, it was discovered that images for abnormal original are greater than 1,200,000 pixels while for small size are less than 35,000 pixels. On the contrary, normal original size are less than 1,200,000 pixels and for small are less than 25,000 pixel. By considering these results, the proposed method can be extended to the iris monitoring system.


  • Features extraction
  • Blood vessels
  • Diabetic

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Wong TY et al (2018) Guidelines on diabetic eye care. Ophthalmology 125(10):1608–1622

    Google Scholar 

  2. Bhatia SK, Atole P, Kamble S, Telang P (2015) Methodology for detecting diabetic presence from iris image analysis. Int J Adv Res Comput Eng & Technol (IJARCET) 4(3)

    Google Scholar 

  3. Hussein SE, Hassan OA, Granat MH (2013) Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks. Biomed Signal Process Control 8:534–541

    Google Scholar 

  4. More SB, Pergad ND (2012) On a methodology for detecting diabetic presence from iris image analysis. In: International Conference on Power, Signals, Controls and Computation 2012 Jan 3, IEEE, pp 1–6

    Google Scholar 

  5. Walvekar M, Salunke G (2015) Detection of diabetic retinopathy with feature extraction using image processing. Int J Emerg Technol Adv Eng 5(1):133–137

    Google Scholar 

  6. Banzi JF, Xue Z (2015) An automated tool for non-contact, real time early detection of diabetes by computer vision. Int J Mach Learn Comput 5(3):225

    Google Scholar 

  7. Xu G, Zhang Z, Ma Y (2008) An image segmentation based method for iris feature extraction. J China Univ Posts Telecommun 15(1):96–117

    Google Scholar 

  8. Abidin ZZ, Manaf M, Shibghatullah AS, Anawar S, Ahmad R (2013) Feature extraction from epigenetic traits using edge detection in iris recognition system. In: IEEE International Conference on Signal and Image Processing Applications, pp 145–149

    Google Scholar 

  9. Samant P, Agarwal R (2017) Diagnosis of diabetes using computer methods: soft computing methods for diabetes detection using iris. Threshold 8:9

    Google Scholar 

  10. Lipton P, Chaturvedi M (2016) Improve the performance of iris recognition using genetic algorithm. Int J Innovations Eng Technol (IJIET) 7(1):296–304

    Google Scholar 

  11. BioBase-Disease-Iris v1.0 is publicly available for research and non-commercial use. See

  12. BioBase-Disease-Iris v2.1 is publicly available for research and non-commercial use. See

Download references


This research work is suported by research grant funded by Universiti Malaysia Pahang (RDU1703233). The authors also would like to thank the Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang for financial support.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Rohana Abdul Karim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Karim, R.A., Mobin, N.A.A.A., Arshad, N.W., Zakaria, N.F., Bakar, M.Z.A. (2020). Early Rubeosis Iridis Detection Using Feature Extraction Process. In: Kasruddin Nasir, A.N., et al. InECCE2019. Lecture Notes in Electrical Engineering, vol 632. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2316-8

  • Online ISBN: 978-981-15-2317-5

  • eBook Packages: EngineeringEngineering (R0)